U.S. patent number 10,448,899 [Application Number 15/335,754] was granted by the patent office on 2019-10-22 for prediction of worsening of heart failure using blended reference.
This patent grant is currently assigned to Cardiac Pacemakers, Inc.. The grantee listed for this patent is Cardiac Pacemakers, Inc.. Invention is credited to Qi An, Viktoria A. Averina, John D. Hatlestad, Pramodsingh Hirasingh Thakur, Yi Zhang.
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United States Patent |
10,448,899 |
Thakur , et al. |
October 22, 2019 |
Prediction of worsening of heart failure using blended
reference
Abstract
Systems and methods for detecting cardiac conditions such as
events indicative of worsening of heart failure (HF) are described.
A system can receive a physiological signal from a patient,
transform one or more first portions of the physiological signal
into respective one or more baseline statistical values, transform
one or more second portions of the physiological signal into one or
more historical extreme values, and generate one or more reference
values of a physiologic parameter using the baseline statistical
values and the historical extreme values. The system can transform
one or more third signal portions of the physiological signal into
respective one or more short-term values, and produce a cardiac
condition indicator using a combination of relative differences
between the short-term values and the corresponding reference
values. The system can output the cardiac condition indicator, or
deliver therapy according to the cardiac condition indicator.
Inventors: |
Thakur; Pramodsingh Hirasingh
(Woodbury, MN), An; Qi (Blaine, MN), Averina; Viktoria
A. (Shoreview, MN), Hatlestad; John D. (Maplewood,
MN), Zhang; Yi (Plymouth, MN) |
Applicant: |
Name |
City |
State |
Country |
Type |
Cardiac Pacemakers, Inc. |
St. Paul |
MN |
US |
|
|
Assignee: |
Cardiac Pacemakers, Inc. (St.
Paul, MN)
|
Family
ID: |
57256456 |
Appl.
No.: |
15/335,754 |
Filed: |
October 27, 2016 |
Prior Publication Data
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Document
Identifier |
Publication Date |
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US 20170119317 A1 |
May 4, 2017 |
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Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
Issue Date |
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62247995 |
Oct 29, 2015 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
A61B
5/7275 (20130101); A61B 5/4836 (20130101); G16H
50/20 (20180101); A61B 5/7282 (20130101); A61B
5/02 (20130101); A61B 7/00 (20130101); A61B
5/053 (20130101); A61B 8/0883 (20130101); A61B
5/7225 (20130101); A61B 7/023 (20130101); A61B
5/02028 (20130101); A61B 7/04 (20130101); G16H
40/63 (20180101); G16H 50/30 (20180101); A61B
5/0535 (20130101); A61B 5/4842 (20130101); A61B
5/04004 (20130101); A61B 5/686 (20130101); A61B
2505/07 (20130101); A61B 5/1102 (20130101) |
Current International
Class: |
A61B
5/00 (20060101); A61B 7/02 (20060101); G16H
40/63 (20180101); G16H 50/20 (20180101); A61B
5/053 (20060101); A61B 5/04 (20060101); A61B
5/02 (20060101); A61B 7/00 (20060101); A61B
7/04 (20060101); A61B 8/08 (20060101); G16H
50/30 (20180101); A61B 5/11 (20060101) |
References Cited
[Referenced By]
U.S. Patent Documents
Foreign Patent Documents
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108366729 |
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Aug 2018 |
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CN |
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WO-2015065674 |
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May 2015 |
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WO |
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WO-2017075154 |
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May 2017 |
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WO |
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Other References
"International Application Serial No. PCT/US2016/059020,
International Preliminary Report on Patentability dated May 11,
2018", 10 pgs. cited by applicant .
"International Application Serial No. PCT/US2016/059020,
International Search Report dated Feb. 2, 2017", 4 pgs. cited by
applicant .
"International Application Serial No. PCT/US2016/059020, Written
Opinion dated Feb. 2, 2017", 8 pgs. cited by applicant.
|
Primary Examiner: Stice; Paula J
Attorney, Agent or Firm: Schwegman Lundberg & Woessner,
P.A.
Parent Case Text
CLAIM OF PRIORITY
This application claims the benefit of priority under 35 U.S.C.
.sctn. 119(e) of U.S. Provisional Patent Application Ser. No.
62/247,995, filed on Oct. 29, 2015, which is herein incorporated by
reference in its entirety.
Claims
What is claimed is:
1. A system, comprising: a signal input circuit configured to
receive at least one physiological signal from a patient; a signal
processor circuit, including: a memory circuit; a reference value
generator circuit including: a filter circuit to transform one or
more first signal portions of the received at least one
physiological signal during respective one or more first time
windows into respective one or more baseline statistical values; a
comparator circuit to transform one or more second signal portions
of the received at least one physiological signal during respective
one or more second time windows into respective one or more
historical extreme values; and a blending circuit to generate one
or more reference values of a physiologic parameter stored in the
memory circuit using the respective one or more baseline
statistical values and the respective one or more historical
extreme values; and a short-term value generator circuit configured
to transform one or more third signal portions of the received at
least one physiological signal during respective one or more third
time windows into respective one or more short-term values stored
in the memory circuit, the one or more third time windows shorter
than the respective first and second time windows; a cardiac
condition detector circuit configured to determine a cardiac
condition indicator using the one or more short-term values and the
one or more reference values; and an output unit, configured to
generate a human-perceptible presentation of an indication of a
progression over time of the cardiac condition indicator.
2. The system of claim 1, wherein: the filter circuit is configured
to produce the one or more baseline statistical values including
respective statistical measures of the physiologic parameter using
the respective one or more first signal portions; and the
comparator circuit is configured to produce the one or more
historical extreme values including respective one or more maxima
or minima of the physiologic parameter using the respective one or
more second signal portions.
3. The system of claim 1, wherein the one or more first time
windows are different from the respective one or more second time
windows by at least one of a window start time, a window end time,
or a window duration.
4. The system of claim 3, wherein at least one of the one or more
second time windows precedes the corresponding first time window in
time.
5. The system of claim 3, wherein at least one of the one or more
second time windows has a longer window duration than the
corresponding first time window.
6. The system of claim 1, wherein: the signal input circuit is
configured to receive the at least one physiologic signal including
a heart sound (HS) signal; the filter circuit is configured to
generate the one or more baseline statistical values including
central tendency of S3 heart sound intensity values using one or
more first signal portions of the received HS signal; and the
comparator circuit is configured to generate the one or more
historical extreme values including minimal S3 heart sound
intensity values using one or more second signal portions of the
received HS signal.
7. The system of claim 1, wherein: the signal input circuit is
configured to receive the at least one physiologic signal including
an impedance (Z) signal; the filter circuit is configured to
generate the one or more baseline statistical values including
central tendency of Z values using one or more first signal
portions of the received Z signal; and the comparator circuit is
configured to generate the one or more historical extreme values
including maximal Z values using one or more second signal portions
of the received Z signal.
8. The system of claim 1, wherein the reference value generator
circuit is further configured to update the one or more reference
values, including one or more of: update the one or more baseline
statistical values using respective one or more specified portions
of the received at least one physiologic signal, the one or more
specified portions postdate the corresponding one or more first
time windows; or update the one or more historical extreme values
using respective one or more updated second time windows, the one
or more updated second time windows differing from the
corresponding second time windows by at least one of a window start
time, a window end time, or a window duration.
9. The system of claim 1, wherein the short-term value generator
circuit is configured to generate the one or more short-term values
using a statistical measure of the respective one or more third
signal portions of the received at least one physiological
signal.
10. The system of claim 1, wherein the cardiac condition detector
circuit is configured to determine the cardiac condition indicator
using a combination of differences between the one or more
short-term values and the corresponding one or more reference
values, each of the differences scaled by a specified weight
factor.
11. The system of claim 1, wherein the cardiac condition detector
circuit is configured to detect an onset of a cardiac condition
when a cardiac condition indicator meets a first criterion, and to
detect a termination of the cardiac condition when the cardiac
condition indicator meets a second criterion.
12. A method, comprising: receiving at least one physiological
signal sensed from a patient using a signal input circuit;
transforming one or more first signal portions of the received at
least one physiological signal during respective one or more first
time windows into respective one or more baseline statistical
values using a filter circuit in a reference value generator
circuit of a signal processor circuit; transforming one or more
second signal portions of the received at least one physiological
signal during respective one or more second time windows into
respective one or more historical extreme values using a comparator
circuit in the reference value generator circuit; generating one or
more reference values of a physiologic parameter stored in a memory
circuit using the respective one or more baseline statistical
values and the respective one or more historical extreme values
using a blending circuit in the reference value generator circuit;
transforming one or more third signal portions of the received at
least one physiological signal during respective one or more third
time windows into respective one or more short-term values stored
in the memory circuit using a short-term value generator circuit of
the signal processor circuit, the one or more third time windows
shorter than the respective first and second time windows;
determining a cardiac condition indicator using the one or more
short-term values and the one or more reference values using a
cardiac condition detector circuit; and generating a
human-perceptible presentation of an indication of a progression
over time of the cardiac condition indicator using an output
unit.
13. The method of claim 12, wherein the one or more baseline
statistical values include respective statistical measures of the
physiologic parameter using the respective one or more first signal
portions, and the one or more historical extreme values including
respective one or more maxima or minima of the physiologic
parameter using the respective one or more second signal
portions.
14. The method of claim 12, wherein the one or more first time
windows are different from the respective one or more second time
windows by at least one of a window start time, a window end time,
or a window duration.
15. The method of claim 12, wherein: receiving the at least one
physiological signal includes receiving a heart sound (HS) signal;
transforming the one or more first signal portions includes
computing central tendency of S3 heart sound intensity values using
one or more first signal portions of the received HS signal; and
transforming the one or more second signal portions includes
identifying one or more minimal S3 heart sound intensity values
from respective one or more second signal portions of the received
HS signal.
16. The method of claim 12, wherein: receiving the at least one
physiological signal includes receiving an impedance (Z) signal;
transforming the one or more first signal portions includes
computing central tendency of Z values using one or more first
signal portions of the received Z signal; and transforming the one
or more second signal portions includes identifying one or more
maximal Z values from respective one or more second signal portions
of the received Z signal.
17. The method of claim 12, further comprising one or more of:
updating the one or more baseline statistical values using
respective one or more specified portions of the received at least
one physiologic signal, the one or more specified portions postdate
the corresponding one or more first time windows; or updating the
one or more historical extreme values using respective one or more
updated second time windows, the one or more updated second time
windows differing from the corresponding second time windows by at
least one of a window start time, a window end time, or a window
duration.
18. The method of claim 12, wherein determining the cardiac
condition indicator includes determining a combination of
differences between the one or more short-term values and the
corresponding one or more reference values, each of the differences
scaled by a specified weight factor.
19. The method of claim 12, wherein the one or more short-term
values include a statistical measure of the respective one or more
third signal portions of the received at least one physiological
signal.
20. The method of claim 12, comprising detecting an onset of a
cardiac condition using the cardiac condition detector circuit when
a cardiac condition indicator meets a first criterion, and
detecting a termination of the cardiac condition using the cardiac
condition detector circuit when the cardiac condition indicator
meets a second criterion.
Description
TECHNICAL FIELD
This document relates generally to medical devices, and more
particularly, to systems, devices and methods for detecting and
monitoring events indicative of worsening of congestive heart
failure.
BACKGROUND
Congestive heart failure (CHF or HF) is a major health problem and
affects many people in the United States alone. CHF patients can
have enlarged heart with weakened cardiac muscles, resulting in
poor cardiac output of blood. Although CHF is usually a chronic
condition, it can occur suddenly. It can affect the left heart,
right heart or both sides of the heart. If CHF affects the left
ventricle, signals that control the left ventricular contraction
are delayed, and the left and right ventricles do not contract
simultaneously. Non-simultaneous contractions of the left and right
ventricles further decrease the pumping efficiency of the
heart.
In many CHF patients, elevated pulmonary vascular pressures can
cause fluid accumulation in the lungs over time. The fluid
accumulation can precede or coincide with worsening HF such as
episodes of HF decompensation. The HF decompensation can be
characterized by pulmonary or peripheral edema, reduced cardiac
output, and symptoms such as fatigue, shortness of breath, and the
like.
OVERVIEW
Frequent monitoring of CHF patients and timely detection of
thoracic fluid accumulation or other events indicative of HF
decompensation status can help prevent worsening HF in CHF
patients, hence reducing cost associated with HF hospitalization.
Additionally, identification of patient at an elevated risk of
developing future events of worsening HF can help ensure timely
treatment, thereby improving the prognosis and patient outcome.
Identifying and safely managing the patients having risk of future
HF events can avoid unnecessary medical intervention and reduce
healthcare cost.
Ambulatory medical devices (AMIDs) can be used for monitoring HF
patient and detecting HF decompensation events. Examples of such
AMDs can include implantable medical devices (IMD), subcutaneous
medical devices, wearable medical devices or other external medical
devices. The AMDs can include, or be communicatively coupled to,
physiologic sensors which can be configured to sense electrical
activity and mechanical function of the heart. The AMDs can deliver
therapy such as electrical stimulations to target tissues or
organs, such as to restore or improve the cardiac function. Some of
these devices can provide diagnostic features, such as using
transthoracic impedance or other sensor signals to detect a disease
or a disease condition. For example, fluid accumulation in the
lungs decreases the transthoracic impedance due to the lower
resistivity of the fluid than air in the lungs. Fluid accumulation
in the lungs can also irritate the pulmonary system and leads to
decrease in tidal volume and increase in respiratory rate.
Prediction of a future HF decompensation event, such as by
detecting a precipitating event such as increased thoracic fluid
accumulation, can be based on a detected change of a sensor signal
(such as a thoracic impedance signal) from a reference signal.
Detection of an event precipitating HF decompensation may be
affected by a number of factors including the choice of physiologic
sensors or physiological signals. For example, a detector using a
physiologic sensor may provide desirable accuracy in HF
decompensation event detection in one patient but less sensitive or
less specific in another patient. The performance of a detector
using a particular sensor signal may change over time such as due
to patient's disease progression, development of a new medical
condition, or other confounding factors attributed to patient's
physiologic responses or environmental noise.
Techniques such as signal filtering or smoothing can be used to
produce a less noisy reference sensor signal, such that a change of
the sensor signal from the reference signal can be more reliably
predictive of future HF decompensation events. However, signal
filtering or smoothing may not be effective in some circumstances,
and may not yield reliable and accurate detection of HF
decompensation, such as when the confounding events or the noise
interferences cause long and sustained changes of sensor signal in
a direction (which is also known as signal drift over time). A
reference signal that is generated using signal smoothing, such as
over a "data-smoothing window", can be disproportionally affected
more by the data characteristics within the data-smoothing window
than by the patient's historical data. The historical sensor data,
associated with patient disease progression and treatment history,
can provide a benchmark of patient health status. Because the
data-smoothing based reference signal may not preserve the power of
the historical sensor data in recognizing the presence or
non-occurrence of the target event (e.g., a HF decompensation
event), undesirably low sensitivity to detection of worsening HF or
inappropriate detection of a termination of a worsened HF status
(or, a detection of improvement of HF status) may result. At least
with these issues in consideration, the present inventors have
recognized that there remains a considerable need for improving HF
decompensation detection in CHF patients using multiple
sensors.
This document discusses, among other things, systems and methods
for detecting cardiac conditions such as events indicative of
worsening HF. A system can include a signal input circuit to sense
a physiological signal from a patient, transform one or more first
portions of the physiological signal into respective one or more
baseline statistical values, transform one or more second portions
of the physiological signal into one or more historical extreme
values, and generate one or more reference values of a physiologic
parameter using the baseline statistical values and the historical
extreme values. The system can transform one or more third signal
portions of the physiological signal into respective one or more
short-term values, and produce a cardiac condition indicator using
a combination of relative differences between the short-term values
and the corresponding reference values. The system can output the
cardiac condition indicator, or deliver therapy according to the
cardiac condition indicator.
In Example 1, a system can comprise a signal input circuit, a
memory circuit, a reference value generator circuit, a short-term
value generator circuit, a cardiac condition detector circuit, and
an output unit. The signal input circuit can include a sense
amplifier circuit to sense at least one physiological signal from a
patient. The reference value generator circuit can be coupled to
the signal input circuit and the memory circuit, and include a
filter circuit to transform one or more first signal portions of
the received at least one physiological signal during respective
one or more first time windows into respective one or more baseline
statistical values, a comparator circuit to transform one or more
second signal portions of the received at least one physiological
signal during respective one or more second time windows into
respective one or more historical extreme values, and a blending
circuit to generate one or more reference values of a physiologic
parameter stored in the memory circuit using the respective one or
more baseline statistical values and the respective one or more
historical extreme values. The short-term value generator circuit
can be coupled to the signal input circuit and the memory circuit
to transform one or more third signal portions of the received at
least one physiological signal during respective one or more third
time windows into respective one or more short-term values stored
in the memory circuit. The one or more third time windows shorter
than the respective first and second time windows. The cardiac
condition detector circuit can be coupled to the memory circuit or
to the reference value generator circuit and short-term value
generator circuits to determine a cardiac condition indicator using
the one or more short-term values and the one or more reference
values. The output unit can generate a human-perceptible
presentation of an indication of a progression over time of the
cardiac condition indicator.
Example 2 can include, or can optionally be combined with the
subject matter of Example 1 to optionally include, the filter
circuit that can produce the baseline statistical values including
respective statistical measures of the physiologic parameter using
the respective one or more first signal portions. The comparator
circuit can produce the one or more historical extreme values
including respective one or more maxima or minima of the
physiologic parameter using the respective one or more second
signal portions.
Example 3 can include, or can optionally be combined with the
subject matter of one or any combination of Examples 1 or 2 to
include, the blending circuit that can generate the one or more
reference values using a linear or a nonlinear combination of at
least one of the one or more baseline statistical values and at
least one of the one or more historical extreme values.
Example 4 can include, or can optionally be combined with the
subject matter of one or any combination of Examples 1 through 3 to
include, the one or more first time windows that are different from
the respective one or more second time windows by at least one of a
window start time, a window end time, or a window duration.
Example 5 can include, or can optionally be combined with the
subject matter of Example 4 to optionally include, at least one of
the one or more second time windows that precedes the corresponding
first time window in time.
Example 6 can include, or can optionally be combined with the
subject matter of one or any combination of Examples 4 or 5 to
include, at least one of the one or more second time windows that
has a longer window duration than the corresponding first time
window.
Example 7 can include, or can optionally be combined with the
subject matter of one or any combination of Examples 1 through 6 to
include, the signal input circuit that can sense a heart sound (HS)
signal, the filter circuit that can generate the one or more
baseline statistical values including central tendency of S3 heart
sound intensity values using one or more first signal portions of
the received HS signal, and the comparator circuit that can
generate the one or more historical extreme values including
minimal S3 heart sound intensity values using one or more second
signal portions of the received HS signal.
Example 8 can include, or can optionally be combined with the
subject matter of one or any combination of Examples 1 through 6 to
include, the signal input circuit that can sense the at least one
physiologic signal including an impedance (Z) signal, the filter
circuit that can generate the one or more baseline statistical
values including central tendency of Z values using one or more
first signal portions of the received Z signal, and the comparator
circuit that can generate the one or more historical extreme values
including maximal Z values using one or more second signal portions
of the received Z signal.
Example 9 can include, or can optionally be combined with the
subject matter of one or any combination of Examples 1 through 8 to
include, the reference value generator circuit that can update the
one or more baseline statistical values using respective one or
more specified portions of the received at least one physiologic
signal. The one or more specified portions postdate the
corresponding one or more first time windows.
Example 10 can include, or can optionally be combined with the
subject matter of one or any combination of Examples 1 through 9 to
include, the reference value generator circuit that can update the
one or more historical extreme values using respective one or more
updated second time windows. The one or more updated second time
windows can be different from the corresponding second time windows
by at least one of a window start time, a window end time, or a
window duration.
Example 11 can include, or can optionally be combined with the
subject matter of one or any combination of Examples 1 through 10
to include, the short-term value generator circuit that can
generate the one or more short-term values using a statistical
measure of the respective one or more third signal portions of the
received at least one physiological signal.
Example 12 can include, or can optionally be combined with the
subject matter of one or any combination of Examples 1 through 11
to include, the cardiac condition detector circuit that can
determine the cardiac condition indicator using a combination of
differences between the one or more short-term values and the
corresponding one or more reference values, where each of the
differences is scaled by a specified weight factor.
Example 13 can include, or can optionally be combined with the
subject matter of one or any combination of Examples 1 through 12
to include, the cardiac condition detector circuit that can
determine the cardiac condition indicator including an indicator of
a future heart failure (HF) decompensation event.
Example 14 can include, or can optionally be combined with the
subject matter of one or any combination of Examples 1 through 13
to include, the cardiac condition detector circuit that can detect
an onset of a cardiac condition when a cardiac condition indicator
meets a first criterion, and a termination of the cardiac condition
when the cardiac condition indicator meets a second criterion.
Example 15 can include, or can optionally be combined with the
subject matter of one or any combination of Examples 1 through 14
to include, a therapy circuit configured to deliver a therapy to
the patient in response to the cardiac condition indicator meeting
a specified condition.
In Example 16, a method can include steps of receiving at least one
physiological signal sensed from a patient, transforming one or
more first signal portions of the received at least one
physiological signal during respective one or more first time
windows into respective one or more baseline statistical values,
and transforming one or more second signal portions of the received
at least one physiological signal during respective one or more
second time windows into respective one or more historical extreme
values. The method can include generating one or more reference
values of a physiologic parameter stored in the memory circuit
using the respective one or more baseline statistical values and
the respective one or more historical extreme values, and
transforming one or more third signal portions of the received at
least one physiological signal during respective one or more third
time windows into respective one or more short-term values stored
in the memory circuit. The one or more third time windows can be
shorter than the respective first and second time windows. The
method can include determining a cardiac condition indicator using
the one or more short-term values and the one or more reference
values, and generating a human-perceptible presentation of an
indication of a progression over time of the cardiac condition
indicator.
Example 17 can include, or can optionally be combined with the
subject matter of Example 16 to optionally include, generating the
one or more baseline statistical values including respective
statistical measures of the physiologic parameter using the
respective one or more first signal portions, and the one or more
historical extreme values including respective one or more maxima
or minima of the physiologic parameter using the respective one or
more second signal portions.
Example 18 can include, or can optionally be combined with the
subject matter of Example 16 to optionally include, the one or more
first time windows used for generating the baseline statistical
values to be different from the respective one or more second time
windows for generating the historical extreme values by at least
one of a window start time, a window end time, or a window
duration.
Example 19 can include, or can optionally be combined with the
subject matter of Example 16 to optionally include, receiving the
at least one physiological signal including a heart sound (HS)
signal, computing central tendency of S3 heart sound intensity
values using one or more first signal portions of the received HS
signal, and identifying one or more minimal S3 heart sound
intensity values from respective one or more second signal portions
of the received HS signal.
Example 20 can include, or can optionally be combined with the
subject matter of Example 16 to optionally include, receiving the
at least one physiological signal including an impedance (Z)
signal, computing central tendency of Z values using one or more
first signal portions of the received Z signal, and identifying one
or more maximal Z values from respective one or more second signal
portions of the received Z signal.
Example 21 can include, or can optionally be combined with the
subject matter of Example 16 to optionally include, updating the
one or more baseline statistical values or updating the one or more
historical extreme values. The baseline statistical values can be
updated using respective one or more specified portions of the
received physiologic signal, where the one or more specified
portions can postdate the corresponding one or more first time
windows. The historical extreme values can be updated using
respective one or more updated second time windows. The updated
second time windows can differ from the corresponding second time
windows by at least one of a window start time, a window end time,
or a window duration.
Example 22 can include, or can optionally be combined with the
subject matter of Example 16 to optionally include, determining the
cardiac condition indicator including a combination of differences
between the one or more short-term values and the corresponding one
or more reference values, where each of the differences is scaled
by a specified weight factor.
Example 23 can include, or can optionally be combined with the
subject matter of Example 16 to optionally include, delivering a
therapy to the patient in response to the cardiac condition
indicator meeting a specified condition.
This Overview is an overview of some of the teachings of the
present application and not intended to be an exclusive or
exhaustive treatment of the present subject matter. Further details
about the present subject matter are found in the detailed
description and appended claims. Other aspects of the invention
will be apparent to persons skilled in the art upon reading and
understanding the following detailed description and viewing the
drawings that form a part thereof, each of which are not to be
taken in a limiting sense. The scope of the present invention is
defined by the appended claims and their legal equivalents.
BRIEF DESCRIPTION OF THE DRAWINGS
Various embodiments are illustrated by way of example in the
figures of the accompanying drawings. Such embodiments are
demonstrative and not intended to be exhaustive or exclusive
embodiments of the present subject matter.
FIG. 1 illustrates generally an example of a cardiac rhythm
management (CRM) system and portions of the environment in which
the CRM system operates.
FIG. 2 illustrates generally an example of a target physiologic
event detector for detecting an event such as a target cardiac
condition.
FIG. 3 illustrates generally an example of a cardiac condition
detector.
FIG. 4 illustrates generally an example of a trend of impedance
measurement over a specified time period.
FIGS. 5A-B illustrates generally examples of an impedance trend
over a period of time and a trend of detection index (DI) for
detecting events of HF decompensation.
FIG. 6 illustrates generally an example of a method for detecting a
target event indicative of progression of cardiac condition in a
patient.
DETAILED DESCRIPTION
Disclosed herein are systems, devices, and methods for detecting
one or more target physiologic events or conditions. The events can
include early precursors of a HF decompensation episode. That is,
these events can occur well before the systematic manifestation of
worsening HF. Therefore, by detecting the precursor events, the
present subject matter can provide a method and device for
detecting an impending HF decompensation episode. The systems,
devices, and methods described herein may be used to determine
cardiac condition such as HF status and/or track progression of the
cardiac condition such as worsening of or recovery from a HF event.
This system can also be used in the context of other diseases
associated with accumulation of thoracic fluid, such as
pneumonia.
FIG. 1 illustrates generally an example of a Cardiac Rhythm
Management (CRM) system 100 and portions of an environment in which
the CRM system 100 can operate. The CRM system 100 can include an
ambulatory medical device, such as an implantable medical device
(IMD) 110 that can be electrically coupled to a heart 105 such as
through one or more leads 108A-C, and an external system 120 that
can communicate with the IMD 110 such as via a communication link
103. The IMD 110 may include an implantable cardiac device such as
a pacemaker, an implantable cardioverter-defibrillator (ICD), or a
cardiac resynchronization therapy defibrillator (CRT-D). The IMD
110 can include one or more monitoring or therapeutic devices such
as an implantable diagnostic device, a wearable external device, a
neural stimulator, a drug delivery device, a biological therapy
device, or one or more other ambulatory medical devices. The IMD
110 may be coupled to, or may be substituted by a monitoring
medical device such as a bedside or other external monitor.
As illustrated in FIG. 1, the IMD 110 can include a hermetically
sealed can housing 112 that can house an electronic circuit that
can sense a physiological signal in the heart 105 and can deliver
one or more therapeutic electrical pulses to a target region, such
as in the heart, such as through one or more leads 108A-C. The CRM
system 100 can include only one lead such as 108B, or can include
two leads such as 108A and 108B.
The lead 108A can include a proximal end that can be configured to
be connected to IMD 110 and a distal end that can be configured to
be placed at a target location such as in the right atrium (RA) 131
of the heart 105. The lead 108A can have a first pacing-sensing
electrode 141 that can be located at or near its distal end, and a
second pacing-sensing electrode 142 that can be located at or near
the electrode 141. The electrodes 141 and 142 can be electrically
connected to the IMD 110 such as via separate conductors in the
lead 108A, such as to allow for sensing of the right atrial
activity and optional delivery of atrial pacing pulses. The lead
108B can be a defibrillation lead that can include a proximal end
that can be connected to IMD 110 and a distal end that can be
placed at a target location such as in the right ventricle (RV) 132
of heart 105. The lead 108B can have a first pacing-sensing
electrode 152 that can be located at distal end, a second
pacing-sensing electrode 153 that can be located near the electrode
152, a first defibrillation coil electrode 154 that can be located
near the electrode 153, and a second defibrillation coil electrode
155 that can be located at a distance from the distal end such as
for superior vena cava (SVC) placement. The electrodes 152 through
155 can be electrically connected to the IMD 110 such as via
separate conductors in the lead 108B. The electrodes 152 and 153
can allow for sensing of a ventricular electrogram and can allow
delivery of one or more ventricular pacing pulses, and electrodes
154 and 155 can allow for delivery of one or more ventricular
cardioversion/defibrillation pulses. In an example, the lead 108B
can include only three electrodes 152, 154 and 155. The electrodes
152 and 154 can be used for sensing or delivery of one or more
ventricular pacing pulses, and the electrodes 154 and 155 can be
used for delivery of one or more ventricular cardioversion or
defibrillation pulses. The lead 108C can include a proximal end
that can be connected to the IMD 110 and a distal end that can be
configured to be placed at a target location such as in a left
ventricle (LV) 134 of the heart 105. The lead 108C may be implanted
through the coronary sinus 133 and may be placed in a coronary vein
over the LV such as to allow for delivery of one or more pacing
pulses to the LV. The lead 108C can include an electrode 161 that
can be located at a distal end of the lead 108C and another
electrode 162 that can be located near the electrode 161. The
electrodes 161 and 162 can be electrically connected to the IMD 110
such as via separate conductors in the lead 108C such as to allow
for sensing of the LV electrogram and allow delivery of one or more
resynchronization pacing pulses from the LV. Additional electrodes
can be included in or along the lead 108C. In an example, as
illustrated in FIG. 1, a third electrode 163 and a fourth electrode
164 can be included in the lead 108. In some examples (not shown in
FIG. 1), at least one of the leads 108A-C, or an additional lead
other than the leads 108A-C, can be implanted under the skin
surface without being within at least one heart chamber, or at or
close to heart tissue.
The IMD 110 can include an electronic circuit that can sense a
physiological signal. The physiological signal can include an
electrogram or a signal representing mechanical function of the
heart 105. The hermetically sealed can housing 112 may function as
an electrode such as for sensing or pulse delivery. For example, an
electrode from one or more of the leads 108A-C may be used together
with the can housing 112 such as for unipolar sensing of an
electrogram or for delivering one or more pacing pulses. A
defibrillation electrode from the lead 108B may be used together
with the can housing 112 such as for delivering one or more
cardioversion/defibrillation pulses. In an example, the IMD 110 can
sense impedance such as between electrodes located on one or more
of the leads 108A-C or the can housing 112. The IMD 110 can be
configured to inject current between a pair of electrodes, sense
the resultant voltage between the same or different pair of
electrodes, and determine impedance using Ohm's Law. The impedance
can be sensed in a bipolar configuration in which the same pair of
electrodes can be used for injecting current and sensing voltage, a
tripolar configuration in which the pair of electrodes for current
injection and the pair of electrodes for voltage sensing can share
a common electrode, or tetrapolar configuration in which the
electrodes used for current injection can be distinct from the
electrodes used for voltage sensing. In an example, the IMD 110 can
be configured to inject current between an electrode on the RV lead
108B and the can housing 112, and to sense the resultant voltage
between the same electrodes or between a different electrode on the
RV lead 108B and the can housing 112. A physiological signal can be
sensed from one or more physiological sensors that can be
integrated within the IMD 110. The IMD 110 can also be configured
to sense a physiological signal from one or more external
physiologic sensors or one or more external electrodes that can be
coupled to the IMD 110. Examples of the physiological signal can
include one or more of thoracic or transthoracic impedance,
intracardiac impedance, arterial pressure, pulmonary artery
pressure, RV pressure, LV coronary pressure, coronary blood
temperature, blood oxygen saturation, one or more heart sounds,
physical activity or exertion level, arrhythmias, posture,
respiration, body weight, or body temperature.
The arrangement and functions of these leads and electrodes are
described above by way of example and not by way of limitation.
Depending on the need of the patient and the capability of the
implantable device, other arrangements and uses of these leads and
electrodes are contemplated.
As illustrated, the CRM system 100 can include a worsening cardiac
condition detector 113. The worsening cardiac condition detector
113 can receive a physiological signal, such as sensed from the
patient using the electrodes on one or more of the leads 108A-C or
the can housing 112, or other physiologic sensors deployed on or
within the patient and communicated with the IMD 110. Examples of
the physiological signals can include thoracic impedance signal,
heart sounds signal, cardiac pressure signals, respiration signals,
among others. The worsening cardiac condition detector 113 can
determine one or more baseline statistical values and one or more
historical extreme values of a physiological parameter using
respectively specified signal portions of the physiological signal,
and generate one or more composite reference values using the
baseline statistical values and the respective historical extreme
values. The worsening cardiac condition detector 113 can transform
one or more portions of the physiological signal into respective
one or more short-term values, and calculate deviations of the
short-term values away from the composite reference values of the
physiological signal, and detect a cardiac condition such as a
worsening HF event from the patient. The worsening HF event can
include one or more early precursors of a HF decompensation
episode, or an event indicative of HF progression such as
deterioration of HF status. The worsening cardiac condition
detector 113 can also be modified to detect recovery of HF status,
or other physiologic events such as pulmonary edema, pneumonia, or
myocardial infarction, among others. Examples of the worsening
cardiac condition detector 113 are described below, such as with
reference to FIGS. 2-3.
The external system 120 can allow for programming of the IMD 110
and can receive information about one or more signals acquired by
IMD 110, such as can be received via a communication link 103. The
external system 120 can include a local external IMD programmer.
The external system 120 can include a remote patient management
system that can monitor patient status or adjust one or more
therapies such as from a remote location.
The communication link 103 can include one or more of an inductive
telemetry link, a radio-frequency telemetry link, or a
telecommunication link, such as an internet connection. The
communication link 103 can provide for data transmission between
the IMD 110 and the external system 120. The transmitted data can
include, for example, real-time physiological data acquired by the
IMD 110, physiological data acquired by and stored in the IMD 110,
therapy history data or data indicating IMD operational status
stored in the IMD 110, one or more programming instructions to the
IMD 110 such as to configure the IMD 110 to perform one or more
actions that can include physiological data acquisition such as
using programmably specifiable sensing electrodes and
configuration, device self-diagnostic test, or delivery of one or
more therapies.
The worsening cardiac condition detector 113 may be implemented in
the external system 120. The external system 120 can be configured
to perform HF decompensation event detection such as using data
extracted from the IMD 110 or data stored in a memory within the
external system 120. Portions of the worsening cardiac condition
detector 113 may be distributed between the IMD 110 and the
external system 120.
Portions of the IMD 110 or the external system 120 can be
implemented using hardware, software, or any combination of
hardware and software. Portions of the IMD 110 or the external
system 120 may be implemented using an application-specific circuit
that can be constructed or configured to perform one or more
particular functions, or can be implemented using a general-purpose
circuit that can be programmed or otherwise configured to perform
one or more particular functions. Such a general-purpose circuit
can include a microprocessor or a portion thereof, a
microcontroller or a portion thereof, or a programmable logic
circuit, or a portion thereof. For example, a "comparator" can
include, among other things, an electronic circuit comparator that
can be constructed to perform the specific function of a comparison
between two signals or the comparator can be implemented as a
portion of a general-purpose circuit that can be driven by a code
instructing a portion of the general-purpose circuit to perform a
comparison between the two signals.
While described with reference to the IMD 110, the CRM system 100
can include a subcutaneous medical device (e.g., subcutaneous
pacemaker or ICD, a subcutaneous monitor, or a subcutaneous
diagnostic device), a wearable medical device (e.g., a patch based
sensing device), or other external medical devices for medical
diagnostics or therapy using various energy sources (e.g.,
electrical, electromagnetic, optical, or mechanical) or therapeutic
agents. The subcutaneous, wearable, or external medical device can
be an untethered device that needs not be tethered to an electrode
or another device by a leadwire or other wired connection (such as
one of the leads 108A-C). The untethered device can include one or
more electrodes on a can housing of the device, or wirelessly
communicate with a sensor or another device associated with the
patient.
FIG. 2 illustrates generally an example of a target physiologic
event detector 200 that can be configured to detect a target
physiologic event from a patient, such as a worsening HF event or
other cardiac conditions. The target physiologic event detector 200
can be an embodiment of the worsening cardiac condition detector
113, and configured to detect worsening HF using at least one
physiological signal sensed from the patient. The target
physiologic event detector 200 can include one or more of a signal
input circuit 210, a signal processor circuit 220, a physiologic
event detector circuit 230, a controller circuit 240, and a user
interface unit 250.
The signal input circuit 210 can include a sense amplifier circuit
to sense a physiological signal sensed from a patient, such as a
physiological signal containing information indicative of status or
progression of HF. In an example, the sense amplifier circuit can
be coupled to one or more electrodes such as the electrodes on one
or more of the leads 108A-C or the can housing 112, one or more
implantable, wearable, or other ambulatory sensors, or one or more
patient monitors. The signal input circuit 210 can include one or
more other sub-circuits to digitize, filter, or perform other
signal conditioning operations on the received physiological
signal. In an example, the signal input circuit 210 can receive one
or more physiological signals from a storage device such as an
electronic medical record (EMR) system, such as in response to a
command signal provided by a system user.
In an example, the signal input circuit 210 can be coupled to one
or more electrodes on one or more of the leads 108A-C or the can
housing 112 to measure an impedance (Z) signal from a patient. The
impedance can include a plurality of measurements of thoracic
impedance or cardiac impedance. The impedance can be produced by
injecting current between a first pair of electrodes and sensing
the resultant voltage across a second pair of electrodes. For
example, the impedance can be sensed across an RA electrode 141 or
142 and the can housing 112 (Z.sub.RA-Can), across an RV electrode
152, 153 or 154 and a can housing 112 (Z.sub.RV-Can), or across an
LV electrode selected from electrodes 161-164 and the can housing
112 (Z.sub.RV-Can). The impedance can include an impedance vector
where the voltage sensing electrodes are the currently injection
electrodes are orthogonal to each other, such as selected from RA,
RV, or LV electrodes (Z.sub.RA-RV-LV).
In another example, the signal input circuit 210 can be coupled to
at least one heart sound (HS) sensor to sense a HS signal from the
patient. The HS sensor can be an implantable, wearable, or
otherwise ambulatory sensor, and placed external to the patient or
implanted inside the body. Examples of the HS sensors can include
an accelerometer, an acoustic sensor, a microphone, a piezo-based
sensor, or other vibrational or acoustic sensors can also be used
to sense the HS signal. The signal input circuit 210 can
alternatively or additionally receive one or more of
electrocardiograph (ECG) or electrograms (EGM), a pulmonary artery
pressure signal, an RV pressure signal, an LV coronary pressure
signal, a coronary blood temperature signal, a blood oxygen
saturation signal, or a respiration signal rate signal or a tidal
volume signal, among others.
The signal processor circuit 220, coupled to the signal input
circuit 210, can generate characteristic values from the received
signal for use in detecting a target cardiac condition such as a
worsening HF event. In an example, the signal processor circuit 220
can be implemented as a part of a microprocessor circuit. The
microprocessor circuit can be a dedicated processor such as a
digital signal processor, application specific integrated circuit
(ASIC), microprocessor, or other type of processor for processing
information including the physiological signals received from the
signal input circuit 210. Alternatively, the microprocessor circuit
can be a general purpose processor that can receive and execute a
set of instructions of performing the functions, methods, or
techniques described herein.
In an example such as illustrated in FIG. 2, the signal processor
circuit 220 can include circuit sets comprising one or more other
circuits or sub-circuits, including a physiologic parameter
generator circuit 221, a reference value generator circuit 222, a
short-term value generator circuit 223, and a memory circuit 224.
The subcircuits may, alone or in combination, perform the
functions, methods, or techniques described herein. In an example,
hardware of the circuit set may be immutably designed to carry out
a specific operation (e.g., hardwired). In an example, the hardware
of the circuit set may include variably connected physical
components (e.g., execution units, transistors, simple circuits,
etc.) including a computer readable medium physically modified
(e.g., magnetically, electrically, moveable placement of invariant
massed particles, etc.) to encode instructions of the specific
operation. In connecting the physical components, the underlying
electrical properties of a hardware constituent are changed, for
example, from an insulator to a conductor or vice versa. The
instructions enable embedded hardware (e.g., the execution units or
a loading mechanism) to create members of the circuit set in
hardware via the variable connections to carry out portions of the
specific operation when in operation. Accordingly, the computer
readable medium is communicatively coupled to the other components
of the circuit set member when the device is operating. In an
example, any of the physical components may be used in more than
one member of more than one circuit set. For example, under
operation, execution units may be used in a first circuit of a
first circuit set at one point in time and reused by a second
circuit in the first circuit set, or by a third circuit in a second
circuit set at a different time.
The physiologic parameter generator circuit 221 can extract from
the sensed physiological signal one or more signal parameters,
including signal mean, median, or other central tendency measures,
a histogram of the signal intensity, or one or more signal trends
over time. In an example, the physiologic parameter generator
circuit 221 can generate a composite signal parameter set such as
using the two or more physiological signals. Examples of the
physiologic parameters can include cardiac or thoracic impedance,
intensity or timing of a HS component such as S1, S2, S3 or S4
heart sound, heart rate, respiration rate, respiration pattern
descriptors such as apnea index indicating the frequency of sleep
apnea, hypopnea index indicating the frequency of sleep hypopnea,
apnea-hypopnea index (AHI) indicating the frequency of or sleep
hypopnea events, or a rapid shallow breathing index (RSBI) which
can be computed as a ratio of respiratory frequency (number of
breaths per minutes) to tidal volume, among others.
In an example, the signal input circuit 210 can receive a thoracic
or cardiac impedance signal according to a specified impedance
sensing configuration, and the physiologic parameter generator
circuit 221 can generate impedance parameters using specified
portions of the received impedance signal, such as during specified
time or during the occurrence of specified physiologic events. For
example, the physiologic parameter generator circuit 221 can
generate the impedance parameters using portions of the received
impedance signal during identical phases of a cardiac cycle (such
as within a certain time window relative to R-wave), or at
identical phases of a respiratory cycle (such as within the
inspiration phase, or the expiration phase). This may minimize or
attenuate the interferences such as due to cardiac or respiratory
activities, in the impedance measurements.
The physiologic parameter generator circuit 221 can generate a
trend of physiologic parameters using impedance measurements
collected during one or more impedance acquisition and analysis
sessions. In an example, an impedance acquisition and analysis
session can start between approximately 5 a.m. and 9 a.m. in the
morning, and lasts for approximately 2-8 hours. In another example,
the impedance acquisition and analysis session can be programmed to
exclude certain time periods, such as night time, or when the
patient is asleep. The impedance parameter can be determined as a
median of multiple impedance measurements acquired during the
impedance acquisition and analysis session. The resultant multiple
impedance parameter values can be used by the reference value
generator circuit 222 and the short-term value generator circuit
223 to generate respective characteristic impedance values. In some
examples, the physiologic parameter generator circuit 221 can sense
two or more physiological signals such as according to two or more
impedance sensing vectors, and can generate a composite impedance
parameter using the two or more physiological signals.
The reference value generator circuit 222 can include a filter
circuit 225, a comparator circuit 226, and a blending circuit 227.
The filter circuit 225 can transform one or more first signal
portions of the received physiological signal during respective one
or more first time windows (W.sub.L1) into respective one or more
baseline statistical values (X.sub.BL). The baseline statistical
values can include statistical measures of a physiologic parameter
using the respective one or more first signal portions during the
first time windows W.sub.L1. Examples of the statistical measure
can include a mean, a median, a mode, a percentile, a quartile, or
other central tendency measures.
The comparator circuit 226 can transform one or more second signal
portions of the received physiological signal during respective one
or more second time windows (W.sub.L2) into respective one or more
historical extreme values (X.sub.XR) of the physiologic parameter.
By way of non-limiting examples, the historical extreme values can
include respective maxima or minima of the physiologic parameter
using the respective one or more second signal portions during the
second time windows W.sub.L2. In some examples, the historical
maxima or the historical minima can be determined from statistical
measures evaluated over various sub-portions of each of the second
time windows W.sub.L2, such as running averages over multiple
sub-windows within each of the second time windows W.sub.L2. The
sub-windows can be non-overlapped from each other, or at least two
of the sub-windows can be overlapped by a specified amount. In an
example, the sub-windows of the second time windows W.sub.L2 can
have substantially similar durations as the first time windows
W.sub.L1.
The baseline statistical values X.sub.BL and the historical extreme
values X.sub.XR can represent different reference values of the
physiologic parameter when the patient is in a low-risk or
risk-free state of developing the target event, such as an event of
worsening HF. In an example, the signal input circuit 210 can be
configured to sense a thoracic impedance signal. The filter circuit
225 can generate one or more baseline statistical impedance values
(Z.sub.BL) each being a central tendency, or other "smoothing"
transformation such as a low-pass filtering, of impedance values
measured during the first time window W.sub.L1. In another example,
the signal input circuit 210 can be configured to sense a HS
signal. The one or more baseline statistical values can include
intensity measures of a HS component such as intensity of S3 heart
sound (.parallel.S3.parallel..sub.BL), each being a central
tendency, or other "smoothing" transformation, of S3 intensity
values measured during multiple cardiac cycles within the time
window W.sub.L1. The statistical measure, such as central tendency
or smoothing transformation over a relatively long window of
W.sub.L1, may exclude abrupt changes in signal parameter value that
may indicate a trend towards worsening HF. As such, the baseline
statistical values X.sub.BL may represent a low-risk state of the
patient developing the target physiologic event.
In an example, the comparator circuit 226 can generate one or more
historical extreme impedance values (Z.sub.XR) each being a maximal
impedance value (Z.sub.max) within a corresponding second time
window W.sub.L2. A larger thoracic impedance may indicate less or
reduced thoracic fluid accumulation, hence a lower likelihood for a
patient to develop future event of worsening HF. Therefore, the
Z.sub.max during W.sub.L2 may represent a historical "risk-free
state" where the patient is least likely to develop a future event
of worsening HF. In an example, the comparator circuit 226 can
generate one or more historical extreme S3 intensity values
(.parallel.S3.parallel..sub.XR), each being a minimal
.parallel.S3.parallel. value (.parallel.S3.parallel..sub.min)
within a corresponding second time window W.sub.L2. A prominent S3
may be a sign of congestive HF, while a weaker or reduced S3
intensity may indicate improved compliance of myocardium and less
oscillation of blood in the ventricles, hence a lower likelihood
for a patient to develop future event of worsening HF. Therefore,
the .parallel.S3.parallel..sub.min during W.sub.L2 may represent a
historical "risk-free state" where the patient is least likely to
develop a future event of worsening HF. Other physiologic
The first time windows W.sub.L1 used in measuring the baseline
statistical values X.sub.BL and the respective second time windows
W.sub.L2 used in measuring the historical extreme values X.sub.XR
can be respectively defined with respect to a reference time
T.sub.Ref, such as the time instant for detecting an event of
worsening cardiac condition. In an example, the target physiologic
event detector 200 can be configured to detect the target
physiologic event regularly or periodically such as on a daily
basis, and the T.sub.Ref can be progressively shifted such as by
one day. In an example, the first time windows W.sub.L1 can be
identical to the respective second time windows W.sub.L2. In
another example, at least one of the first time windows W.sub.L1
can differ from the respective second time window W.sub.L2 by at
least one of a window start time, a window end time, or a window
duration. In an example, at least one of the second time windows
W.sub.L2 can precede the corresponding first time window W.sub.L1
in time. In another example, at least one of the second time
windows W.sub.L2 can have a longer window duration than the
corresponding first time window W.sub.L1. By way of non-limiting
example, the W.sub.L1 can begin 90 days prior to T.sub.Ref and end
60 days prior to T.sub.Ref, denoted as "90-60 days". Other examples
of W.sub.L1 can include 60-30 days, 80-10 days, 80-20 days, 60-20
days, or 40-20 days prior to T.sub.Ref. Examples of W.sub.L2 can
include a time duration expires 2 years, 1 year, or 6 months prior
to T.sub.Ref. Examples of measuring the baseline statistical values
and the historical extremes values are discussed below, such as
with reference to FIG. 4.
The blending circuit 227 can use the one or more baseline
statistical values X.sub.BL and the respective one or more
historical extreme values X.sub.XR to generate one or more
reference values (X.sub.Ref) of the physiologic parameter. The
reference values X.sub.Ref can be a linear or a nonlinear
combination of one or more baseline statistical values
{X.sub.BL(i)} each measured during corresponding first time window
{W.sub.L1(i)}, and one or more historical extreme values
{X.sub.XR(j)} each measured during corresponding second time
windows {W.sub.L2(j)}, that is: X.sub.Ref=f({X.sub.BL(i)},
{X.sub.XR(j)} (1) where f is a linear or nonlinear function. For
example, X.sub.Ref can be a weighted sum of N baseline statistical
values measured from N first time windows, and M historical extreme
values measured from M second windows, that is,
X.sub.Ref=a.sub.1*X.sub.BL(1)+a.sub.2*X.sub.BL(2)+ . . .
+a.sub.N*X.sub.BL(N)+b.sub.1*X.sub.XR(1)+b.sub.2*X.sub.XR(2)+ . . .
+b.sub.N*X.sub.XR(M) (2) where a.sub.i and b.sub.j are weight
factors for the respective baseline statistical value X.sub.BL(i)
and the historical extreme value X.sub.XR(j).
In an example, the reference value generator circuit 222 can update
one or more of the baseline statistical values X.sub.BL using
respective one or more specified portions of the received at least
one physiological signal. The one or more specified portions can
postdate the corresponding one or more first time windows. As a
result, the more recent information contained in the physiological
signal can be included into the baseline statistical values. In an
example, the reference value generator circuit 222 can periodically
(such as according to a specified baseline value update frequency),
or upon receiving a user's command, update X.sub.BL using a linear
combination of historically computed X.sub.BL and the parameter
values obtained from the more recent portions of the physiological
signal.
Additionally or alternatively, the reference value generator
circuit 222 can initiate a process of updating one or more of the
historical extreme values X.sub.XR using updated second time
windows W.sub.L2', which may differ from the corresponding second
time windows W.sub.L2 by at least one of a window start time, a
window end time, or a window duration. In an example, the reference
value generator circuit 222 can update X.sub.XR upon receiving a
command from a system user, or upon receiving an indication that a
specified condition has been fulfilled, such as a detection of an
improved cardiac condition. The update of X.sub.XR can be less
frequent than the update of the X.sub.BL. In an example, X.sub.BL
can be updated daily, and X.sub.XR can be updated weekly, monthly,
quarterly, or yearly.
The short-term value generator circuit 223 can transform one or
more third signal portions of the received physiological signal
during respective one or more third time windows (W.sub.S) into
respective one or more short-term values (X.sub.S) stored in the
memory circuit. In an example, the short-term value generator
circuit 223 can generate the one or more short-term values X.sub.S
using a statistical measure of the respective one or more second
signal portions. Examples of the statistical measures can include a
mean, a median, a mode, a percentile, a quartile, or other measures
of central tendency measures. In an example, at least some of the
third time windows Ws can have shorter window duration than the
respective first and second time windows W.sub.L1 and W.sub.L2. In
an example, the third time windows W.sub.S can be approximately 24
hours, 2-10 days, or 14-28 days in duration. In some examples, some
of the first time windows W.sub.L1 or the second time windows
W.sub.L2 precede the corresponding third time windows W.sub.S in
time.
The memory circuit 224 can be coupled to the reference value
generator circuit 222, and store one or more of the baseline
statistical values X.sub.BL, the historical extreme values
X.sub.XR, or the reference values X.sub.Ref such as produced by the
blending circuit 227. The memory circuit 224 can also be coupled to
the short-term value generator circuit 223 to store short-term
values X.sub.S.
The physiologic event detector circuit 230 can be configured to
detect a target physiologic event or condition, such as a
physiologic event indicative of an onset of a disease, worsening of
a disease state, or a change of a disease state. In an example, the
physiologic event detector circuit 230 can detect the presence of
an event indicative of HF decompensation status, worsening HF,
pulmonary edema, pneumonia, or myocardial infarction, among others.
In some examples, the physiologic event detector circuit 230 can
generate a detection index (DI) using the one or more reference
values produced by the reference value generator circuit 222 and
the one or more short-term values produced by the short-term value
generator circuit 223. In an example, the physiologic event
detector circuit 230 can compute the DI using a combination of the
differences between the one or more short-term values (X.sub.S) and
corresponding one or more reference values (X.sub.Ref), where the
differences can be scaled by respective weight factors. The DI can
represent the trend of the physiologic parameter over time, such as
accumulated deviations from reference values, and can indicate
presence or severity of a physiologic condition precipitating a HF
decompensation event, such as excessive thoracic fluid
accumulation. Examples of computing the DI and using DI to detect a
cardiac condition are discussed below, such as with reference to
FIG. 3.
The controller circuit 240 can control the operations of the signal
input circuit 210, the signal processor circuit 220, the
physiologic event detector circuit 230, and the data and
instruction flow between these components. In an example, the
controller circuit 240 can control the settings of electrical
impedance sensing including, for example, selecting electrodes used
for current injection and the electrodes used for sensing the
resultant voltage, or a beginning and an end of an impedance
acquisition and analysis session. In another example, the
controller circuit 240 can initiate an impedance acquisition and
analysis session in response to a detection of a triggering event
such as a change of a physiologic state or a change of the
patient's health condition, or a specific time of a day such as in
the morning between 6 a.m. and 12 noon. Alternatively, the
controller circuit 240 can use an indication of a sleep-to-awake
state transition to initiate an impedance acquisition and analysis
session for acquiring impedance measurement during specified time
following the transition to the awake state.
The user interface unit 250 can be configured to present
programming options to the user and receive user's programming
input. The user interface unit 250 can include an input device,
such as a keyboard, on-screen keyboard, mouse, trackball, touchpad,
touch-screen, or other pointing or navigating devices. The input
device can enable a system user to program the parameters used for
sensing the physiological signals. The user interface can include
an output unit that can produce a presentation of information
including the detected progression of cardiac condition. The
information can be presented in a table, a chart, a diagram, or any
other types of textual, tabular, or graphical presentation formats,
for displaying to a system user. The presentation of the output
information can include audio or other human-perceptible media
format to alert the system user of the detected progression of
cardiac condition. In an example, at least a portion of the user
interface unit 250, such as the user interface, can be implemented
in the external system 120.
FIG. 3 illustrates generally an example of a cardiac condition
detector 330, which can be an example of the physiologic event
detector circuit 230 of the target physiologic event detector 200
in FIG. 2. The cardiac condition detector 330 can include one or
more of a deviation calculator 331, an accumulator circuit 332, and
a comparator circuit 333.
The deviation calculator 331 can be coupled to the reference value
generator circuit 222 and the short-term value calculator 227, and
configured to compute relative deviations (.DELTA.X) of the one or
more short-term values X.sub.S, such as provided by the short-term
value calculator 227, from the corresponding one or more reference
values X.sub.Ref, such as provided by the reference value generator
circuit 222. Examples of the relative deviations can include
differences, percentile change, or other relative difference
measures. In an example, .DELTA.X=X.sub.Ref-X.sub.S. In another
example, .DELTA.X=(X.sub.Ref-X.sub.S)/X.sub.Ref.
The accumulator 332 can compute a detection index (DI) using a
combination of at least some of the relative deviations, each
scaled by a corresponding weight factor. In an example, the DI can
be computed as a weighted sum of the deviations (.DELTA.X), that
is, DI=.SIGMA..sub.i=1.sup.N.omega..sub.i.DELTA.X.sub.i. When the
reference values X.sub.Ref is computed using both the baseline
statistical values X.sub.BL and the historical extreme values
X.sub.XR, the deviation .DELTA.X, such as computed as
.DELTA.X=X.sub.Ref-X.sub.S=f(X.sub.BL, X.sub.XR)-X.sub.S, may
include information about deviations of X.sub.S from the baseline
statistical values X.sub.BL, and deviations of X.sub.S from the
historical extreme values X.sub.BL. As previously discussed with
reference to FIG. 2, the baseline statistical values X.sub.BL and
the historical extreme values X.sub.XR can represent different
reference values when the patient is in low-risk or risk-free
states of developing the target physiologic event. By incorporating
the historical reference values X.sub.XR into the reference value
X.sub.Ref, the resultant DI includes information about deviation
from the "risk-free state" characterized by X.sub.XR. For example,
a short-term impedance value Zs that is lower than the historical
Z.sub.max, or a short-term S3 intensity
.parallel.S3.parallel..sub.S higher than the historical
.parallel.S3.parallel..sub.min, may respectively indicate a trend
towards an increased risk of developing a future event of worsening
HF.
The comparator 333 can compare the DI, such as produced by the
accumulator 334, to a criterion such as a threshold value or a
specified range. The comparator 333 can generate an indication of
detecting a target event such as worsening HF if the DI exceeds the
threshold or falls within a specified range. In an example, the DI
can be compared to a first threshold to detect an onset of the
target event, and compared to a second threshold to detect a
termination of the target event. The second threshold can be the
same as, or different from, the first threshold. The first and
second thresholds can be respectively provided by a system user
such as via the user interface unit 250. Alternatively, at least
one of the first or second thresholds can be automatically
determined as a specified fraction of one of the baseline
statistical value X.sub.BL, the historical extreme value X.sub.XR
(such as Z.sub.Max or .parallel.S3.parallel..sub.Min), or the
composite reference value X.sub.Ref. In an example, the first
threshold can be a first percentage or fraction of the X.sub.BL, or
the second threshold can be a percentage or a fraction of the
X.sub.XR. Examples of computing the DI using the weighted
accumulation are discussed below, such as with reference to FIGS.
4-5.
FIG. 4 illustrates generally an example of a trend 400 of impedance
measurement (as shown on they-axis) calculated over time (as shown
on the x-axis), such as over approximately 70 days. The impedance
values can be acquired by an impedance sensing circuit, such as the
signal input circuit 210, within or communicatively coupled to an
implantable medical device (IMD). The impedance sensing circuit can
be configured to couple to one or more electrodes on the RV lead
and the IMD housing and to acquire measurements from the RV-Can
impedance vector (Z.sub.RV-Can). Each impedance measurement,
denoted by data points 410 in the trend 400, represents a
characteristic impedance value (such as a median, a mean, or other
statistical value) during a 24-hour impedance acquisition and
analysis session. The representative impedance value can be
generated such as by an impedance sensing circuit coupled to the
signal input circuit 210. The impedance signal can be used to
detect an event of worsening HF, such as a HF decompensation
event.
A first time window 420, a second time window 430, and a short-term
time window 440 of the representative impedance values can be
respectively defined. The filter circuit 225 can be used to
establish a baseline statistical impedance value, Z.sub.BL, such as
a mean or a median of the impedance data within the first time
window 420. The comparator circuit 226 can be used to measure a
historical extreme impedance value, such as the maximal impedance
value Z.sub.max at 435, from the impedance data within the second
time window 430. In an example, the historical maximal impedance
value Z.sub.max can be determined from statistical measures
evaluated over various sub-portions of the second time window 430.
The second window 430 can include multiple sub-windows each having
a substantially similar duration as the first window 420. Adjacent
sub-windows can be overlapped from each other, and the subsequent
sub-window is obtained by forward-shifting the previous sub-window
such as by one day. A statistical measure (such as an average
impedance) can be computed for each sub-window, and the maximal
impedance value Z.sub.max can be determined as the maximum of the
statistical measures corresponding to some or all of the
sub-windows.
The short-term value generator circuit 223A can be used to
establish a short-term impedance value Zs, such as a mean or median
of the impedance data within the short-term time window 440. The
baseline statistical impedance value Z.sub.BL and the maximal
impedance value Z.sub.max can be combined (such as using a weighted
sum) at the blending circuit 227 to produce a reference impedance
Z.sub.Ref. A detection decision can be made, such as by the
physiologic event detector circuit 230 or the cardiac condition
detector circuit 330, when a comparison between the short-term
impedance value Zs and the reference impedance value Z.sub.Ref
meets a specified condition.
FIGS. 5A-B illustrate generally examples of a thoracic impedance
(Z) signal and a trend of detection index (DI) for detecting events
of HF decompensation. The trend of DI can be used to detect a
worsening HF event such as by using the physiologic event detector
circuit 230 or any variant thereof, such as the cardiac condition
detector circuit 330. The detected worsening HF event can be
presented to a system user such as via a display unit in the user
interface unit 250
FIG. 5A illustrates an impedance trend 510 that can include
representative impedance values (shown on they-axis) over a period
of time (shown on the x-axis). The impedance can be sensed
according a specified impedance vector that includes one or more
electrodes on one or more of the implantable leads such as 108A-C
or the can housing 112 implanted or otherwise attached to the
patient. A portion of the trend 510 has a time span of
approximately 12 months. Each data point in the trend 510 indicates
a representative impedance value, which can be computed (such as by
the physiologic parameter generator circuit 221) as a mean, a
median, or other statistics of impedance measurements during a
specified impedance acquisition session, such as a 24-hour
session.
The impedance trend 510 reaches its maximal value 515 in an earlier
phase of the timeframe shown in FIG. 5A. The impedance subsequently
decays until reaching the minimal impedance 516, from which the
impedance slowly recovers (increases) as indicated by an upward
trend 517. A baseline statistical impedance value Z.sub.BL can be
computed as a mean, median, or other central tendency measure of
impedance measurements within a first long-term time window
W.sub.L1. By way of non-limiting example, the window W.sub.L1 is
defined to be 40 days to 10 days prior to the reference time
T.sub.Ref at which the DI is to be determined. The baseline
statistical impedance value Z.sub.BL can be updated periodically
using a linear combination of Z.sub.BL computed from an old window
and the daily impedance value. A historical extreme impedance
value, such as a historical maximal impedance value Z.sub.Max, can
be determined using impedance data within a second long-term time
window W.sub.L2. By way of non-limiting example, the window
W.sub.L2 has a duration that expires 12 months prior to T.sub.Ref.
The W.sub.L2 is therefore long enough to include the maximal
impedance 515, which occurs within 12 months until T.sub.Ref. A
composite reference Z.sub.Ref can be computed as, for example, a
weighted combination of Z.sub.BL and Z.sub.Max. A short-term
impedance values Z.sub.S can be computed within one or more
short-term time windows each having a duration of, for example, 24
hours.
FIG. 5B illustrates a DI trend 520 indicating DI values (data
points on the DI trend 520), as shown on the y-axis, over time as
shown on the x-axis. Each DI value can be computed using the
physiologic event detector circuit 230 or any variant thereof, such
as the cardiac condition detector circuit 330. A deviation
(.DELTA.Z) of Zs from the reference Z.sub.Ref
(.DELTA.Z=Z.sub.Ref-Z.sub.S) can be computed, and the DI value can
be computed as cumulative deviations over multiple short-term
windows. The trend of DI can then be used to detect a worsening HF
event. A positive DI value (i.e., above the zero line 521)
indicates that accumulatively, the short-term impedance values
(Z.sub.S) are lower than the reference impedance value (Z.sub.Ref).
Such a decrease in thoracic impedance may indicate an increase in
thoracic fluid accumulation, a precursor of worsening HF.
Conversely, a negative DI value represents an accumulative increase
of Z.sub.S that exceeds the reference Z.sub.Ref, which may indicate
a reduced thoracic fluid, an indication of improved HF status, or
termination of a previously detected worsening HF event.
As illustrated in FIG. 5B, the DI trend can be compared to a DI
onset threshold 522 to detect an onset of the event of worsening
HF, and compared to a DI termination threshold 523 to detect
termination of the detected event of worsening HF. A first
worsening HF event 531 is detected prior to the impedance reaches
minimal value 516, with an onset at 525A when the DI exceeds the DI
onset threshold 522 and a termination at 525B when the DI falls
below the DI termination threshold 523. Following the minimal value
516A, a second worsening HF event 532 is detected with an onset at
526A when the DI exceeds the DI onset threshold 522, and a
termination at 526B when the DI falls below the DI termination
threshold 523. The impedance has recovered during this time as
indicated by the trend 517, such that the short-term impedance
Z.sub.S may exceed the baseline statistical impedance Z.sub.BL.
However, the reference impedance value Z.sub.Ref during the
impedance recovery phase can be dominated by the historical extreme
value Z.sub.Max 515. As a result, even though Z.sub.BL is lower
than Z.sub.S, the reference Z.sub.Ref may maintain at a level
greater than Z.sub.S. The deviation .DELTA.Z=Z.sub.Ref-Z.sub.S may
still be above the termination threshold 523, such that the second
worsening HF event 532 remain to be detected during the impedance
recovery period, until the deviation .DELTA.Z falls below the
threshold 523 at 526B.
The DI onset threshold 522 and the DI termination threshold 523 can
be automatically determined as a specified fraction of one of the
reference value Z.sub.Ref, the baseline statistical value Z.sub.BL,
or the historical extreme value Z.sub.XR (such as Z.sub.Max). In an
example, the DI onset threshold 522 can be based on Z.sub.BL, and
the DI termination threshold 523 can be based on Z.sub.Max. In an
example, as illustrated in FIG. 5B, the DI onset threshold 522 can
be higher than the DI termination threshold 523. The detected
worsening HF events 531 and 532, including the onset and
termination time, can be presented to a system user such as via a
display unit in the user interface unit 250.
FIG. 6 illustrates generally an example of a method 600 for
detecting a target event indicative of progression of cardiac
condition in a patient. The target event can include a HF
decompensation event, an event indicative of worsening HF, or an
event indicative of recovery from a HF condition. The method 600
can be implemented and operate in an ambulatory medical device such
as an implantable or wearable medical device, or in a remote
patient management system. In an example, the IMD 110 or the
external system 120, including its various examples discussed in
this document, can be programmed to perform method 600, including
its various examples discussed in this document.
The method 600 begins at 610 by receiving at least one
physiological signal from a patient. Examples of the physiological
signal can include one or more of an electrocardiograph (ECG) or
electrogram (EGM) such as sensed from electrodes on one or more of
the leads 108A-C or the can housing 112, a impedance signal, an
arterial pressure signal, a pulmonary artery pressure signal, an RV
pressure signal, an LV coronary pressure signal, a coronary blood
temperature signal, a blood oxygen saturation signal, a heart sound
(HS) signal, or a respiration signal rate signal or a tidal volume
signal, among others. In an example, a thoracic or cardiac
impedance signal can be sensed according a specified impedance
vector that includes one or more electrodes on one or more of the
implantable leads such as 108A-C or the can housing 112 implanted
or otherwise attached to the patient. The impedance can be sensed
in response to a detection of a triggering event such as a change
of a physiologic state, a change of the patient's health condition,
or a specific time of a day such as when the patient is awake.
The sensed impedance can be pre-processed, including one or more of
signal amplification, digitization, filtering, or other signal
conditioning operations. One or more statistical or morphological
signal metrics can be extracted from the pre-processed signal.
At 620, one or more first signal portions of the received at least
one physiological signal during respective one or more first time
windows (W.sub.L1) can be transformed into respective one or more
baseline statistical values (X.sub.BL), such as by using the filter
circuit 225. The X.sub.BL can be a statistical measure--such as a
mean, a median, a mode, a percentile, a quartile, or other central
tendency measures--of a physiologic parameter using the respective
one or more first signal portions during the one or more first time
windows W.sub.L1. The first time windows W.sub.L1 can be defined
with respect to a reference time T.sub.Ref, such as the time
instant for detecting an event of worsening cardiac condition. Each
of the first time windows W.sub.L1 can be defined by one or more of
a window start time, a window end time, and a window duration.
At 630, one or more second signal portions of the received at least
one physiological signal during respective one or more second time
windows (W.sub.L2) can be transformed into respective one or more
historical extreme values (X.sub.XR), such as using the comparator
circuit 226. By way of non-limiting examples, the X.sub.XR can
include respective maxima or minima of the physiologic parameter
using the respective one or more second signal portions during the
one or more second time windows W.sub.L2. Similar to the W.sub.L1,
the W.sub.L2 can be defined with respect to a reference time
T.sub.Ref, such as the time instant for detecting an event of
worsening cardiac condition. The first time windows W.sub.L1 can be
identical to the respective second time windows W.sub.L2.
Alternatively, at least one of the first time windows W.sub.L2 can
differ from the respective second time window W.sub.L2 by at least
one of a window start time, a window end time, or a window
duration. In an example, as illustrated in FIG. 4, at least one of
the W.sub.L2 can precede the corresponding first time window
W.sub.L1 in time, or have a longer window duration than the
corresponding first time window W.sub.L1. The baseline statistical
values X.sub.BL may represent a low-risk state of the patient
developing the target physiologic event.
In an example, the physiological signal received at 610 includes a
heart sound (HS) signal, such as sensed by using a HS sensor. One
or more baseline statistical values of S3 heart sound intensity
(.parallel.S3.parallel..sub.BL) can be generated at 620. One or
more historical extreme S3 intensity values
(.parallel.S3.parallel..sub.XR), each being a minimal S311 value
(.parallel.S3.parallel..sub.min) within a corresponding second time
window W.sub.L2, can be generated at 630. A prominent S3 may be
predictive of congestive HF, while a smaller or reduced S3
intensity may indicate improved compliance of myocardium and less
oscillation of blood in the ventricles, hence a lower likelihood
for a patient to develop future event of worsening HF. In an
example, the physiological signal received at 610 includes a
thoracic impedance signal. One or more baseline statistical
impedance values (Z.sub.BL) can be generated at 620. One or more
historical extreme impedance values (Z.sub.XR), each being a
maximal impedance value (Z.sub.max) within a corresponding second
time window W.sub.L2, can be generated at 630. A larger thoracic
impedance may indicate less or reduced thoracic fluid accumulation,
hence a lower likelihood for a patient to develop future event of
worsening HF. The .parallel.S3.parallel..sub.min during W.sub.L2,
or the Z.sub.max during W.sub.L, may represent a historical
"risk-free state" where the patient is least likely to develop a
future event of worsening HF.
The baseline statistical values X.sub.BL, or the historical extreme
values X.sub.XR, can be regularly or periodically updated. One or
more of the X.sub.BL can be updated using respective one or more
specified portions of the received at least one physiologic signal,
where the one or more specified portions can postdate the
corresponding one or more first time windows. One or more of the
X.sub.XR can be updated using respective one or more updated second
time windows, where the one or more updated second time windows
differing from the corresponding second time windows by at least
one of a window start time, a window end time, or a window
duration.
At 640, one or more reference values (X.sub.Ref) of a physiologic
parameter can be generated, such as by using the blending circuit
227, by combining the respective one or more baseline statistical
values X.sub.BL and the respective one or more historical extreme
values X.sub.XR. The reference values X.sub.Ref can be a linear or
a nonlinear combination of one or more baseline statistical values
X.sub.BL and one or more historical extreme values X.sub.XR.
At 650, one or more third signal portions of the received at least
one physiological signal, during respective one or more third time
windows (W.sub.S), can be transformed into respective one or more
short-term values (X.sub.S). The one or more third time windows can
be shorter than the respective first and second time windows. In an
example, at least some of the third time windows W.sub.S can have
shorter window duration than the respective first and second time
windows W.sub.L1 and W.sub.L2. In some examples, some of the first
time windows W.sub.L1 or the second time windows W.sub.L2 precede
the corresponding third time windows Ws in time.
At 660, a cardiac condition indicator can be produced using the one
or more short-term values (X.sub.S) and the one or more reference
values (X.sub.Ref). The cardiac condition indicator can indicate
presence of an event indicative of HF decompensation status,
worsening HF, pulmonary edema, pneumonia, or myocardial infarction,
among others. In an example, the cardiac condition indicator can be
computed as a combination of the differences between the one or
more short-term values (X.sub.S) and corresponding one or more
reference values (X.sub.Ref), where the differences can be scaled
by respective weight factors. In an example, the cardiac condition
indicator can be computed as accumulation of deviations of the
short-term thoracic impedance values (Z.sub.S) from the reference
impedance values (Z.sub.Ref), and can indicate presence or severity
of a physiologic condition precipitating a HF decompensation event,
such as excessive thoracic fluid accumulation. A target cardiac
condition, such as worsening HF, is deemed detected if the cardiac
condition indicator exceeds the threshold or falls within a
specified range.
At 670, information including the detection of the progression of
cardiac condition indicator can be presented to the system user in
a human-perceptible format in an output unit, such as a display or
a user interface unit 250. In an example, the output information
can be presented in a table, a chart, a diagram, or any other types
of textual, tabular, or graphical presentation formats. In an
example, an alert can be produced if a worsening HF is detected.
The alert can be in audio or other human-perceptible media format.
The method 600 can additionally include delivering a therapy, such
as electrostimulation therapy delivered to the heart, a nerve
tissue, or other target tissues in response to the detection of a
worsening HF event.
The above detailed description includes references to the
accompanying drawings, which form a part of the detailed
description. The drawings show, by way of illustration, specific
embodiments in which the invention can be practiced. These
embodiments are also referred to herein as "examples." Such
examples can include elements in addition to those shown or
described. However, the present inventors also contemplate examples
in which only those elements shown or described are provided.
Moreover, the present inventors also contemplate examples using any
combination or permutation of those elements shown or described (or
one or more aspects thereof), either with respect to a particular
example (or one or more aspects thereof), or with respect to other
examples (or one or more aspects thereof) shown or described
herein.
In the event of inconsistent usages between this document and any
documents so incorporated by reference, the usage in this document
controls.
In this document, the terms "a" or "an" are used, as is common in
patent documents, to include one or more than one, independent of
any other instances or usages of "at least one" or "one or more."
In this document, the term "or" is used to refer to a nonexclusive
or, such that "A or B" includes "A but not B," "B but not A," and
"A and B," unless otherwise indicated. In this document, the terms
"including" and "in which" are used as the plain-English
equivalents of the respective terms "comprising" and "wherein."
Also, in the following claims, the terms "including" and
"comprising" are open-ended, that is, a system, device, article,
composition, formulation, or process that includes elements in
addition to those listed after such a term in a claim are still
deemed to fall within the scope of that claim. Moreover, in the
following claims, the terms "first," "second," and "third," etc.
are used merely as labels, and are not intended to impose numerical
requirements on their objects.
Method examples described herein can be machine or
computer-implemented at least in part. Some examples can include a
computer-readable medium or machine-readable medium encoded with
instructions operable to configure an electronic device to perform
methods as described in the above examples. An implementation of
such methods can include code, such as microcode, assembly language
code, a higher-level language code, or the like. Such code can
include computer readable instructions for performing various
methods. The code may form portions of computer program products.
Further, in an example, the code can be tangibly stored on one or
more volatile, non-transitory, or non-volatile tangible
computer-readable media, such as during execution or at other
times. Examples of these tangible computer-readable media can
include, but are not limited to, hard disks, removable magnetic
disks, removable optical disks (e.g., compact disks and digital
video disks), magnetic cassettes, memory cards or sticks, random
access memories (RAMs), read only memories (ROMs), and the
like.
The above description is intended to be illustrative, and not
restrictive. For example, the above-described examples (or one or
more aspects thereof) may be used in combination with each other.
Other embodiments can be used, such as by one of ordinary skill in
the art upon reviewing the above description. The Abstract is
provided to comply with 37 C.F.R. .sctn. 1.72(b), to allow the
reader to quickly ascertain the nature of the technical disclosure.
It is submitted with the understanding that it will not be used to
interpret or limit the scope or meaning of the claims. Also, in the
above Detailed Description, various features may be grouped
together to streamline the disclosure. This should not be
interpreted as intending that an unclaimed disclosed feature is
essential to any claim. Rather, inventive subject matter may lie in
less than all features of a particular disclosed embodiment. Thus,
the following claims are hereby incorporated into the Detailed
Description as examples or embodiments, with each claim standing on
its own as a separate embodiment, and it is contemplated that such
embodiments can be combined with each other in various combinations
or permutations. The scope of the invention should be determined
with reference to the appended claims, along with the full scope of
equivalents to which such claims are entitled.
* * * * *